R Proyecto Final

#Modificar variable para especificar directorio del Proyecto Final
#local.path <- "/Users/akcasill/Downloads"
local.path <- "/Users/jos/Downloads"

#Dependencies
#install.packages("png")
install.packages("rid")

Warning in install.packages :
  package ‘rid’ is not available (for R version 3.6.3)

library(png)

#ASISTENCIAS TOTALES

setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

# son 9 semestres de 6 materias cada uno. 
# 1.- Asistencias Totales
load("AsistenciasTotales.R")
class(asistencias.totales)

[1] "list"

length(asistencias.totales)

[1] 1000

class(asistencias.totales[[1]])

[1] "matrix"

dim(asistencias.totales[[1]])

[1] 32 54

class(asistencias.totales[1])

[1] "list"

asistencias.totales[[1]][1:10,1:10]

      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
 [1,]    2    2    2    2    2    2    1    2    2     2
 [2,]    2    2    2    2    2    0    2    2    2     2
 [3,]    2    2    2    2    2    2    2    0    2     2
 [4,]    2    2    2    2    2    2    2    0    2     2
 [5,]    2    1    2    1    2    1    2    2    2     2
 [6,]    2    1    2    2    2    0    0    0    2     2
 [7,]    1    2    2    1    2    0    2    2    2     2
 [8,]    2    2    2    0    2    1    1    1    2     2
 [9,]    2    2    2    0    1    2    2    0    2     2
[10,]    2    2    2    2    2    2    2    0    0     2

#Asistencias
#===================
#Definición Valores
#===================
# 2 El alumno tiene asistnecia completa.
# 1 El alumno tiene retardo.
# 0 El alumno tiene falta.

#Sólo tomar las primeras 12 materias (Columnas)
for(i in 1:length(asistencias.totales)){
  asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
}
asistencias.totales[[4]]

      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
 [1,]    2    2    2    2    2    2    0    2    2     2     2     2
 [2,]    2    2    2    2    2    1    2    2    2     2     2     2
 [3,]    2    2    2    2    0    2    2    1    2     2     2     1
 [4,]    2    2    2    2    2    2    2    2    2     2     2     2
 [5,]    2    1    2    2    2    2    2    2    2     2     2     2
 [6,]    2    1    2    2    1    1    2    1    2     2     1     2
 [7,]    1    2    2    2    2    1    2    2    2     2     2     2
 [8,]    2    2    2    2    1    2    1    0    2     2     2     2
 [9,]    2    2    2    1    0    2    2    1    0     2     2     2
[10,]    2    2    2    2    2    2    2    2    1     2     2     2
[11,]    2    2    2    2    2    2    2    2    2     2     2     2
[12,]    2    2    2    2    2    2    2    2    2     2     2     2
[13,]    2    2    2    2    2    2    2    2    1     2     2     2
[14,]    2    2    2    1    2    2    2    2    2     2     2     2
[15,]    2    2    2    2    2    2    2    2    2     2     2     2
[16,]    2    2    2    2    2    2    2    0    2     2     2     2
[17,]    2    1    2    1    2    2    2    2    2     2     2     2
[18,]    0    2    2    2    1    2    2    2    1     2     2     2
[19,]    2    2    2    1    2    2    0    2    2     2     2     2
[20,]    2    2    2    2    1    2    2    2    2     2     2     2
[21,]    1    2    2    2    1    1    2    2    1     2     2     2
[22,]    2    2    2    0    1    2    2    2    2     2     2     2
[23,]    2    2    2    2    2    2    0    2    2     2     2     2
[24,]    2    2    2    2    2    2    2    2    2     2     2     2
[25,]    2    2    2    2    2    2    0    2    2     2     2     2
[26,]    2    2    2    2    2    2    2    0    1     2     2     2
[27,]    2    2    2    2    2    2    2    2    2     2     2     2
[28,]    2    2    2    2    0    2    2    2    2     2     2     2
[29,]    2    1    2    2    2    2    1    2    1     2     2     2
[30,]    2    2    2    2    0    2    2    1    0     2     2     2
[31,]    2    2    2    2    2    2    2    2    2     2     2     2
[32,]    2    2    2    2    2    2    2    2    1     2     2     2

Tamaño de Lista de Asistencia de Alumnos:

length(asistencias.totales)

[1] 1000

Lista de total Asistencias por Alumno

#
#for(i in 1:length(asistencias.totales)){
#  asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
#}
asistencias.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(asistencias.totales)){
  for(j in 1:12){
    asistencias.alumnos[i,j] <- sum(asistencias.totales[[i]][,j])/32    
  }
}

asistencias.alumnos[1,]

 [1] 1.87500 1.87500 2.00000 1.40625 1.90625 1.43750 1.59375 1.12500 1.84375 2.00000 1.96875 1.96875

asistencias.df <- as.data.frame(asistencias.alumnos)

#Asistencia Materias Ejemplo: AM1 = Asistencia Materia 1
colnames(asistencias.df) <- c('AM1','AM2','AM3','AM4','AM5','AM6','AM7','AM8','AM9','AM10','AM11','AM12')
#DATA FRAME DE ASISTENCIAS ALUMNOS
#=================================
#Suma de asistencias por Materia
#=================================
asistencias.df

#PERFIL ALUMNOS

setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

print("Summary")

[1] "Summary"

load("perfilAlumnos.R")
#head(perfil.alumnos,1)
str(perfil.alumnos)

'data.frame':   1000 obs. of  7 variables:
 $ genero                  : int  2 2 2 1 2 2 2 2 1 2 ...
 $ admision.letras         : num  60.1 59.1 53.1 57 61.5 ...
 $ admision.numeros        : num  35.2 33.2 21.3 29 37.9 ...
 $ promedio.preparatoria   : num  70.3 67.2 60 61 74.4 ...
 $ edad.ingreso            : num  18 17 15 16 18 18 15 17 14 17 ...
 $ evalucion.socioeconomica: int  4 4 4 4 4 4 4 4 4 4 ...
 $ nota.conducta           : num  16 15 13 14 16 16 13 15 12 15 ...

summary(perfil.alumnos)

     genero      admision.letras admision.numeros promedio.preparatoria  edad.ingreso   evalucion.socioeconomica nota.conducta  
 Min.   :1.000   Min.   :44.94   Min.   : 4.878   Min.   : 60.00        Min.   :11.00   Min.   :1.000            Min.   : 9.00  
 1st Qu.:1.000   1st Qu.:56.61   1st Qu.:28.226   1st Qu.: 60.00        1st Qu.:16.00   1st Qu.:3.000            1st Qu.:14.00  
 Median :2.000   Median :59.98   Median :34.970   Median : 69.95        Median :17.00   Median :4.000            Median :15.00  
 Mean   :1.595   Mean   :60.06   Mean   :35.114   Mean   : 72.25        Mean   :17.53   Mean   :3.466            Mean   :15.53  
 3rd Qu.:2.000   3rd Qu.:63.64   3rd Qu.:42.275   3rd Qu.: 80.91        3rd Qu.:19.00   3rd Qu.:4.000            3rd Qu.:17.00  
 Max.   :2.000   Max.   :77.71   Max.   :70.411   Max.   :100.00        Max.   :25.00   Max.   :4.000            Max.   :20.00  

#===================
#Definición Valores
#===================
# Genero: 2 Hombre, 1 Mujer.
# admision.letras: Calificación Examen Admisión Español
# admision.numeros: Calificación Examen Admisión Matemáticas
# promedio.preparatoria: Calificación Promedio Preparatoria   
# edad.ingreso: Edad, variable numérica             
# evalucion.socioeconomica: 1 más privilegiado, 4 menos privilagiado
# nota.conducta: Calificación subjetiva. 

perfil.alumnos$genero <- factor(perfil.alumnos$genero)
perfil.alumnos$evalucion.socioeconomica <-
  factor(perfil.alumnos$evalucion.socioeconomica)

perfil.alumnos$edad.ingreso <- 
  factor(perfil.alumnos$edad.ingreso)

#DATAFRAME CALIFICACIONES ALUMNOS

setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

print("Summary")

[1] "Summary"

# 3 1000 matrices de 2 x 54, calificación entre 1 y 20
load("ResultadosExamenes.R")
#resultados.examenes.totales

examenes.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(resultados.examenes.totales)){
  for(j in 1:12){
    examenes.alumnos[i,j] <- sum(resultados.examenes.totales[[i]][,j])/2    
  }
}

examenes.alumnos[1,]

 [1] 11.956449 12.330884 12.463337 15.189492 12.328150 17.087821  9.466637 12.011178 11.368753 12.221370 11.416652 12.330704

cal.alumnos.df <- as.data.frame(examenes.alumnos)
#Calificaciones Materias Ejemplo: CM2 = Calificiación Promedio Materia 2
colnames(cal.alumnos.df) <- c('CM1','CM2','CM3','CM4','CM5','CM6','CM7','CM8','CM9','CM10','CM11','CM12')
#===================
#Definición Valores
#===================
# CM1: Calificación Materia 1 valor Máximo 20
cal.alumnos.df

#TRABAJOS POR CLASE

setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

print("Summary")

[1] "Summary"

# 4 1000 matrices de 4 x 54, son 4 trabajos por clase, entre 1 y 20
load("ResultadoTrabajos.R")
resultados.trabajos.totales[[2]][,1]

[1] 11.79653 12.11637 12.71856 13.72462

tareas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(resultados.trabajos.totales)){
  for(j in 1:12){
    tareas.alumnos[i,j] <- sum(resultados.trabajos.totales[[i]][,j])/4    
  }
}

#tareas.alumnos[1,]
tareas.alumnos.df <- as.data.frame(tareas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(tareas.alumnos.df) <- c('TM1','TM2','TM3','TM4','TM5','TM6','TM7','TM8','TM9','TM10','TM11','TM12')
#===================
#Definición Valores
#===================
# TM1: Calificación Tarea Materia 1 valor Máximo 20
tareas.alumnos.df

#VISITAS BIBLIOTECA

# 5 Redondear. Uso físico y virtual. vector. 1000 Matrices, número de veces que asistio a la biblioteca por materia
setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

load("UsoBiblioteca.R")
length(uso.biblioteca.totales)

[1] 1000

mi.val <- uso.biblioteca.totales[[1]][1,1]
mi.val

[1] 12.65509

mi.val <- as.data.frame(mi.val)
mi.val

visitas.biblio.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    visitas.biblio.alumnos[i,j] <- uso.biblioteca.totales[[i]][1,j]
  }
}

visitas.biblio.alumnos.df <- as.data.frame(visitas.biblio.alumnos)
#Visitas Biblioteca  Ejemplo: VBM2 = Visitas Biblioteca  Materia 2
colnames(visitas.biblio.alumnos.df) <- c('VBM1','VBM2','VBM3','VBM4','VBM5','VBM6','VBM7','VBM8','VBM9','VBM10','VBM11','VBM12')
#===================
#Definición Valores
#===================
# VBM1: Visitas Biblioteca Materia 1
visitas.biblio.alumnos.df

NA
NA

#USO DE PLATAFORMAS DIGITALES

# 6 Redondear, vector. Uso de Canvas o de Plataforma digital.
setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

load("UsoPlataforma.R")
#uso.plataforma.totales
uso.plataforma.totales[[1]][,1:12]

 [1] 32.796526 32.554647 32.504125 79.290015 32.600643 80.313415  5.944546 33.398886 32.664804 33.522435 32.831749 32.208083

plataformas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    plataformas.alumnos[i,j] <- uso.plataforma.totales[[i]][1,j]
  }
}

#tareas.alumnos[1,]
plataformas.alumnos.df <- as.data.frame(plataformas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(plataformas.alumnos.df) <- c('PDM1','PDM2','PDM3','PDM4','PDM5','PDM6','PDM7','PDM8','PDM9','PDM10','PDM11','PDM12')
#===================
#Definición Valores
#===================
# PDM1: Plataformas Digitales Materia 1 valor Máximo 20
plataformas.alumnos.df

NA

#APARTADO DE LIBROS POR MATERIA

# 7
setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

load("ApartadoDeLibros.R") #1000 matrices, cantidad de libros que el alumno reservó por materia.
separacion.libros.totales[[1]][,1:12]

 [1] 1 1 1 3 1 3 0 1 1 1 1 1

reserva.libros.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    reserva.libros.alumnos[i,j] <- separacion.libros.totales[[i]][1,j]
  }
}

reserva.libros.alumnos.df <- as.data.frame(reserva.libros.alumnos)
#Reserva de Libris Ejemplo: RLM2 = Reserva de Libros Por Materia 2
colnames(reserva.libros.alumnos.df) <- c('RLM1','RLM2','RLM3','RLM4','RLM5','RLM6','RLM7','RLM8','RLM9','RLM10','RLM11','RLM12')
#===================
#Definición Valores
#===================
# RLM1: Reserva de Libros pro Materia 1
reserva.libros.alumnos.df

NA

#DISTRIBUCIÓN DE BECAS ALUMNOS

# 8 vector binario, 1 tiene beca, 0 no tiene Beca
setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

load("Becas.R")
distribucion.becas[[1]]

[1] 0

sum(distribucion.becas)

[1] 163

becas.alumnos <- matrix(1:1000, nrow=1000, ncol=1)

for(i in 1:1000){
    becas.alumnos[i] <- distribucion.becas[i]
}

becas.alumnos.df <- as.data.frame(becas.alumnos)
colnames(becas.alumnos.df) <- c('BECA')
#===================
#Definición Valores
#===================
# BECA: Tiene Beca 1
becas.alumnos.df

#Necesita ser un factor por que solo tiene dos valores 0 o 1 
becas.alumnos.df$BECA <- as.factor(becas.alumnos.df$BECA)
becas.alumnos.df

#HISTORIAL DE PAGOS ALUMNOS

# 9  2 en tiempo, 1 retraso, 0, Son 9 semestres pero hay que user sólo 2 primeras columnas, 4 pagos.
setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

load("HistorialPagos.R")
length(registro.pagos)

[1] 1000

registro.pagos[[500]]

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,]    2    2    2    2    2    2    1    2    2
[2,]    2    1    1    2    2    2    2    2    2
[3,]    2    2    2    2    1    2    2    1    2
[4,]    1    2    2    2    2    2    2    1    2

pagos.alumnos <- matrix(1:2000, nrow=1000, ncol=2)

for(i in 1:1000){
  for(j in 1:2){
    pagos.alumnos[i,j] <- sum(registro.pagos[[i]][,j])/4    
  }
}

#tareas.alumnos[1,]
pagos.alumnos.df <- as.data.frame(pagos.alumnos)
#Pago Semestre: PSEM2 = Pago Semestre 2
colnames(pagos.alumnos.df) <- c('PSEM1','PSEM2')
#===================
#Definición Valores
#===================
# PSEM1: Suma de pagos semestre 1, 2 valor máximo.
pagos.alumnos.df

NA

datos.alumnos.df <- cbind.data.frame(perfil.alumnos,
                                     becas.alumnos.df,
                              asistencias.df,
                              cal.alumnos.df,
                              tareas.alumnos.df,
                              visitas.biblio.alumnos.df,
                              plataformas.alumnos.df,
                              reserva.libros.alumnos.df,
                              pagos.alumnos.df)
datos.alumnos.df

NA

str(datos.alumnos.df)

'data.frame':   1000 obs. of  82 variables:
 $ genero                  : Factor w/ 2 levels "1","2": 2 2 2 1 2 2 2 2 1 2 ...
 $ admision.letras         : num  60.1 59.1 53.1 57 61.5 ...
 $ admision.numeros        : num  35.2 33.2 21.3 29 37.9 ...
 $ promedio.preparatoria   : num  70.3 67.2 60 61 74.4 ...
 $ edad.ingreso            : Factor w/ 15 levels "11","12","13",..: 8 7 5 6 8 8 5 7 4 7 ...
 $ evalucion.socioeconomica: Factor w/ 4 levels "1","2","3","4": 4 4 4 4 4 4 4 4 4 4 ...
 $ nota.conducta           : num  16 15 13 14 16 16 13 15 12 15 ...
 $ BECA                    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ AM1                     : num  1.88 1.88 1.88 1.88 1.88 ...
 $ AM2                     : num  1.88 1.41 1.41 1.88 1.41 ...
 $ AM3                     : num  2 2 2 2 1.72 ...
 $ AM4                     : num  1.41 1.81 1.81 1.81 1.81 ...
 $ AM5                     : num  1.91 1.5 1.38 1.56 1.91 ...
 $ AM6                     : num  1.44 1.38 1.12 1.88 1.12 ...
 $ AM7                     : num  1.59 1.69 1.69 1.69 1.69 ...
 $ AM8                     : num  1.12 1.12 1.94 1.69 1.12 ...
 $ AM9                     : num  1.84 1.84 1.84 1.66 1.66 ...
 $ AM10                    : num  2 2 1.22 2 2 ...
 $ AM11                    : num  1.97 1.97 1.97 1.97 1.97 ...
 $ AM12                    : num  1.97 1.97 1.97 1.97 1.97 ...
 $ CM1                     : num  12 12 12 12 12 ...
 $ CM2                     : num  12.3 12.3 15.8 15.8 12.3 ...
 $ CM3                     : num  12.5 12.5 12.5 16 16 ...
 $ CM4                     : num  15.2 11.9 11.9 15.2 11.9 ...
 $ CM5                     : num  12.3 15.8 12.3 12.3 12.3 ...
 $ CM6                     : num  17.1 17.1 13.3 17.1 13.3 ...
 $ CM7                     : num  9.47 13.08 13.08 13.08 13.08 ...
 $ CM8                     : num  12.01 15.35 12.01 7.69 15.35 ...
 $ CM9                     : num  11.37 11.37 11.37 18.25 6.61 ...
 $ CM10                    : num  12.2 12.2 12.2 15.6 12.2 ...
 $ CM11                    : num  11.4 11.4 11.4 11.4 11.4 ...
 $ CM12                    : num  12.3 12.3 12.3 15.8 12.3 ...
 $ TM1                     : num  12.6 12.6 12.6 12.6 12.6 ...
 $ TM2                     : num  12.2 12.2 15.6 15.6 12.2 ...
 $ TM3                     : num  12.3 12.3 12.3 15.7 15.7 ...
 $ TM4                     : num  15.2 11.9 11.9 15.2 11.9 ...
 $ TM5                     : num  12.6 16.1 12.6 12.6 12.6 ...
 $ TM6                     : num  16.2 16.2 12.6 16.2 12.6 ...
 $ TM7                     : num  7.97 12.18 12.18 12.18 12.18 ...
 $ TM8                     : num  12.59 16.13 12.59 8.66 16.13 ...
 $ TM9                     : num  11.5 11.5 11.5 18.33 6.84 ...
 $ TM10                    : num  12.5 12.5 12.5 15.9 12.5 ...
 $ TM11                    : num  11.6 11.6 11.6 11.6 11.6 ...
 $ TM12                    : num  12.6 12.6 12.6 16.1 12.6 ...
 $ VBM1                    : num  12.7 12.7 12.7 12.7 12.7 ...
 $ VBM2                    : num  11.8 11.8 27.8 27.8 11.8 ...
 $ VBM3                    : num  11.7 11.7 11.7 27.5 27.5 ...
 $ VBM4                    : num  33.8 15.9 15.9 33.8 15.9 ...
 $ VBM5                    : num  12 28 12 12 12 ...
 $ VBM6                    : num  34.1 34.1 16.1 34.1 16.1 ...
 $ VBM7                    : num  2.98 19.89 19.89 19.89 19.89 ...
 $ VBM8                    : num  14.66 31.99 14.66 1.93 31.99 ...
 $ VBM9                    : num  12.22 12.22 12.22 62.22 1.44 ...
 $ VBM10                   : num  15.1 15.1 15.1 32.6 15.1 ...
 $ VBM11                   : num  12.8 12.8 12.8 12.8 12.8 ...
 $ VBM12                   : num  10.7 10.7 10.7 26 10.7 ...
 $ PDM1                    : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM2                    : num  32.6 32.6 59.2 59.2 32.6 ...
 $ PDM3                    : num  32.5 32.5 32.5 58.4 58.4 ...
 $ PDM4                    : num  79.3 33.8 33.8 79.3 33.8 ...
 $ PDM5                    : num  32.6 60 32.6 32.6 32.6 ...
 $ PDM6                    : num  80.3 80.3 33.8 80.3 33.8 ...
 $ PDM7                    : num  5.94 34.97 34.97 34.97 34.97 ...
 $ PDM8                    : num  33.4 73.31 33.4 3.33 73.31 ...
 $ PDM9                    : num  32.66 32.66 32.66 161.08 2.11 ...
 $ PDM10                   : num  33.5 33.5 33.5 75.4 33.5 ...
 $ PDM11                   : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM12                   : num  32.2 32.2 32.2 53.5 32.2 ...
 $ RLM1                    : num  1 1 1 1 1 1 4 2 1 1 ...
 $ RLM2                    : num  1 1 2 2 1 0 2 1 1 2 ...
 $ RLM3                    : num  1 1 1 2 2 2 2 2 1 2 ...
 $ RLM4                    : num  3 1 1 3 1 3 3 3 1 1 ...
 $ RLM5                    : num  1 2 1 1 1 1 2 1 1 2 ...
 $ RLM6                    : num  3 3 1 3 1 5 0 3 1 3 ...
 $ RLM7                    : num  0 1 1 1 1 5 3 1 1 3 ...
 $ RLM8                    : num  1 2 1 0 2 1 4 0 1 2 ...
 $ RLM9                    : num  1 1 1 4 0 1 2 2 1 1 ...
 $ RLM10                   : num  1 1 1 3 1 3 3 3 5 3 ...
 $ RLM11                   : num  1 1 1 1 1 1 2 1 1 1 ...
 $ RLM12                   : num  1 1 1 2 1 0 2 1 1 1 ...
 $ PSEM1                   : num  2 2 2 2 2 2 1.5 2 2 2 ...
 $ PSEM2                   : num  2 1.5 1.5 2 2 1.5 2 2 2 2 ...

summary(datos.alumnos.df)

 genero  admision.letras admision.numeros promedio.preparatoria  edad.ingreso evalucion.socioeconomica nota.conducta   BECA         AM1             AM2       
 1:405   Min.   :44.94   Min.   : 4.878   Min.   : 60.00        17     :200   1: 56                    Min.   : 9.00   0:837   Min.   :1.875   Min.   :1.406  
 2:595   1st Qu.:56.61   1st Qu.:28.226   1st Qu.: 60.00        18     :167   2:107                    1st Qu.:14.00   1:163   1st Qu.:1.875   1st Qu.:1.875  
         Median :59.98   Median :34.970   Median : 69.95        19     :166   3:152                    Median :15.00           Median :1.875   Median :1.875  
         Mean   :60.06   Mean   :35.114   Mean   : 72.25        16     :140   4:685                    Mean   :15.53           Mean   :1.875   Mean   :1.779  
         3rd Qu.:63.64   3rd Qu.:42.275   3rd Qu.: 80.91        20     :113                            3rd Qu.:17.00           3rd Qu.:1.875   3rd Qu.:1.875  
         Max.   :77.71   Max.   :70.411   Max.   :100.00        15     : 98                            Max.   :20.00           Max.   :1.875   Max.   :1.875  
                                                                (Other):116                                                                                   
      AM3             AM4             AM5             AM6             AM7             AM8             AM9             AM10            AM11            AM12      
 Min.   :1.031   Min.   :1.031   Min.   :1.375   Min.   :1.125   Min.   :1.406   Min.   :1.125   Min.   :1.562   Min.   :1.219   Min.   :1.562   Min.   :1.438  
 1st Qu.:1.719   1st Qu.:1.406   1st Qu.:1.562   1st Qu.:1.438   1st Qu.:1.594   1st Qu.:1.875   1st Qu.:1.797   1st Qu.:2.000   1st Qu.:1.969   1st Qu.:1.969  
 Median :2.000   Median :1.812   Median :1.906   Median :1.875   Median :1.688   Median :1.938   Median :1.844   Median :2.000   Median :1.969   Median :1.969  
 Mean   :1.837   Mean   :1.658   Mean   :1.794   Mean   :1.720   Mean   :1.632   Mean   :1.801   Mean   :1.781   Mean   :1.872   Mean   :1.892   Mean   :1.875  
 3rd Qu.:2.000   3rd Qu.:1.812   3rd Qu.:1.906   3rd Qu.:1.875   3rd Qu.:1.688   3rd Qu.:1.938   3rd Qu.:1.844   3rd Qu.:2.000   3rd Qu.:1.969   3rd Qu.:1.969  
 Max.   :2.000   Max.   :1.812   Max.   :1.906   Max.   :1.875   Max.   :1.688   Max.   :1.938   Max.   :1.844   Max.   :2.000   Max.   :1.969   Max.   :1.969  
                                                                                                                                                                
      CM1              CM2              CM3              CM4              CM5              CM6             CM7              CM8              CM9        
 Min.   : 7.594   Min.   : 8.218   Min.   : 8.439   Min.   : 7.487   Min.   : 8.214   Min.   : 9.86   Min.   : 9.467   Min.   : 7.685   Min.   : 6.615  
 1st Qu.:11.956   1st Qu.:12.331   1st Qu.:12.463   1st Qu.:11.892   1st Qu.:12.328   1st Qu.:13.32   1st Qu.:13.080   1st Qu.:12.011   1st Qu.:11.369  
 Median :11.956   Median :12.331   Median :12.463   Median :11.892   Median :12.328   Median :13.32   Median :13.080   Median :12.011   Median :11.369  
 Mean   :12.751   Mean   :13.567   Mean   :13.701   Mean   :13.064   Mean   :13.582   Mean   :14.72   Mean   :14.600   Mean   :13.233   Mean   :12.493  
 3rd Qu.:11.956   3rd Qu.:15.775   3rd Qu.:15.951   3rd Qu.:15.189   3rd Qu.:15.771   3rd Qu.:17.09   3rd Qu.:16.773   3rd Qu.:15.348   3rd Qu.:14.492  
 Max.   :18.638   Max.   :18.887   Max.   :18.976   Max.   :18.595   Max.   :18.885   Max.   :19.54   Max.   :19.387   Max.   :18.674   Max.   :18.246  
                                                                                                                                                        
      CM10             CM11             CM12             TM1              TM2              TM3             TM4              TM5              TM6        
 Min.   : 8.036   Min.   : 6.694   Min.   : 8.218   Min.   : 8.648   Min.   : 8.036   Min.   : 8.11   Min.   : 7.457   Min.   : 8.608   Min.   : 8.735  
 1st Qu.:12.221   1st Qu.:11.417   1st Qu.:12.331   1st Qu.:12.589   1st Qu.:12.221   1st Qu.:12.27   1st Qu.:11.874   1st Qu.:12.565   1st Qu.:12.641  
 Median :12.221   Median :11.417   Median :12.331   Median :12.589   Median :12.221   Median :12.27   Median :11.874   Median :12.565   Median :12.641  
 Mean   :13.488   Mean   :12.466   Mean   :13.539   Mean   :13.375   Mean   :13.437   Mean   :13.47   Mean   :13.043   Mean   :13.865   Mean   :13.931  
 3rd Qu.:15.628   3rd Qu.:14.556   3rd Qu.:15.774   3rd Qu.:12.589   3rd Qu.:15.629   3rd Qu.:15.69   3rd Qu.:15.166   3rd Qu.:16.087   3rd Qu.:16.188  
 Max.   :18.814   Max.   :18.278   Max.   :18.887   Max.   :19.059   Max.   :18.814   Max.   :18.84   Max.   :18.583   Max.   :19.043   Max.   :19.094  
                                                                                                                                                        
      TM7              TM8              TM9              TM10             TM11             TM12             VBM1             VBM2            VBM3       
 Min.   : 7.965   Min.   : 8.657   Min.   : 6.836   Min.   : 8.418   Min.   : 7.003   Min.   : 8.624   Min.   : 1.531   Min.   : 1.37   Min.   : 1.336  
 1st Qu.:12.179   1st Qu.:12.594   1st Qu.:11.502   1st Qu.:12.451   1st Qu.:11.602   1st Qu.:12.574   1st Qu.:12.655   1st Qu.:11.85   1st Qu.:11.680  
 Median :12.179   Median :12.594   Median :11.502   Median :12.451   Median :11.602   Median :12.574   Median :12.655   Median :11.85   Median :11.680  
 Mean   :13.539   Mean   :13.920   Mean   :12.650   Mean   :13.758   Mean   :12.688   Mean   :13.829   Mean   :19.024   Mean   :19.30   Mean   :19.149  
 3rd Qu.:15.572   3rd Qu.:16.126   3rd Qu.:14.669   3rd Qu.:15.934   3rd Qu.:14.802   3rd Qu.:16.099   3rd Qu.:12.655   3rd Qu.:27.77   3rd Qu.:27.521  
 Max.   :18.786   Max.   :19.063   Max.   :18.334   Max.   :18.967   Max.   :18.401   Max.   :19.050   Max.   :62.655   Max.   :61.85   Max.   :61.680  
                                                                                                                                                        
      VBM4             VBM5            VBM6             VBM7             VBM8             VBM9            VBM10            VBM11            VBM12       
 Min.   : 2.172   Min.   : 1.40   Min.   : 2.213   Min.   : 2.978   Min.   : 1.933   Min.   : 1.443   Min.   : 2.015   Min.   : 1.554   Min.   : 1.139  
 1st Qu.:15.858   1st Qu.:12.00   1st Qu.:16.063   1st Qu.:19.889   1st Qu.:14.663   1st Qu.:12.216   1st Qu.:15.075   1st Qu.:12.773   1st Qu.:10.694  
 Median :15.858   Median :12.00   Median :16.063   Median :19.889   Median :14.663   Median :12.216   Median :15.075   Median :12.773   Median :10.694  
 Mean   :23.968   Mean   :19.66   Mean   :24.112   Mean   :29.373   Mean   :22.640   Mean   :19.961   Mean   :23.304   Mean   :20.444   Mean   :17.920  
 3rd Qu.:33.787   3rd Qu.:28.00   3rd Qu.:34.094   3rd Qu.:39.834   3rd Qu.:31.994   3rd Qu.:28.324   3rd Qu.:32.612   3rd Qu.:29.159   3rd Qu.:26.040  
 Max.   :65.858   Max.   :62.00   Max.   :66.063   Max.   :69.889   Max.   :64.663   Max.   :62.216   Max.   :65.075   Max.   :62.773   Max.   :60.694  
                                                                                                                                                        
      PDM1              PDM2              PDM3             PDM4              PDM5              PDM6              PDM7              PDM8              PDM9        
 Min.   :  2.328   Min.   :  1.924   Min.   :  1.84   Min.   :  3.929   Min.   :  2.001   Min.   :  4.031   Min.   :  5.945   Min.   :  3.331   Min.   :  2.108  
 1st Qu.: 32.797   1st Qu.: 32.555   1st Qu.: 32.50   1st Qu.: 33.757   1st Qu.: 32.601   1st Qu.: 33.819   1st Qu.: 34.967   1st Qu.: 33.399   1st Qu.: 32.665  
 Median : 32.797   Median : 32.555   Median : 32.50   Median : 33.757   Median : 32.601   Median : 33.819   Median : 34.967   Median : 33.399   Median : 32.665  
 Mean   : 49.085   Mean   : 45.384   Mean   : 45.65   Mean   : 55.804   Mean   : 46.282   Mean   : 56.079   Mean   : 68.347   Mean   : 52.866   Mean   : 47.051  
 3rd Qu.: 32.797   3rd Qu.: 59.244   3rd Qu.: 58.40   3rd Qu.: 79.290   3rd Qu.: 60.011   3rd Qu.: 80.313   3rd Qu.: 99.445   3rd Qu.: 73.315   3rd Qu.: 61.080  
 Max.   :163.275   Max.   :159.244   Max.   :158.40   Max.   :179.290   Max.   :160.011   Max.   :180.313   Max.   :199.445   Max.   :173.315   Max.   :161.080  
                                                                                                                                                                 
     PDM10             PDM11             PDM12              RLM1            RLM2            RLM3            RLM4            RLM5            RLM6      
 Min.   :  3.537   Min.   :  2.386   Min.   :  1.347   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.: 33.522   1st Qu.: 32.832   1st Qu.: 32.208   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
 Median : 33.522   Median : 32.832   Median : 32.208   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000  
 Mean   : 54.494   Mean   : 47.882   Mean   : 42.551   Mean   :1.373   Mean   :1.425   Mean   :1.429   Mean   :1.878   Mean   :1.436   Mean   :1.866  
 3rd Qu.: 75.374   3rd Qu.: 63.862   3rd Qu.: 53.468   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000  
 Max.   :175.374   Max.   :163.862   Max.   :153.468   Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :5.000   Max.   :4.000   Max.   :5.000  
                                                                                                                                                      
      RLM7            RLM8            RLM9           RLM10           RLM11           RLM12           PSEM1           PSEM2     
 Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :1.500   Min.   :1.50  
 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.750   1st Qu.:1.75  
 Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :2.000   Median :2.00  
 Mean   :1.955   Mean   :1.441   Mean   :1.445   Mean   :1.895   Mean   :1.429   Mean   :1.419   Mean   :1.892   Mean   :1.90  
 3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.00  
 Max.   :5.000   Max.   :4.000   Max.   :4.000   Max.   :5.000   Max.   :4.000   Max.   :4.000   Max.   :2.000   Max.   :2.00  
                                                                                                                               

datos.integrados <- datos.alumnos.df

setwd(local.path)

The working directory was changed to /Users/jos/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

save(datos.integrados, file="datos.integrados.R")
getwd()

[1] "/Users/jos/Downloads"

load("datos.integrados.R")
datos.integrados

head(datos.integrados)

NA

#Separar 100 alumnos que no entraran en Kmeans

set.seed(1234)

ind <- sample(x=c(0,1),size=nrow(datos.integrados),
              replace=TRUE,prob = c(0.9,0.1))
ind

   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  [80] 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1
 [159] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [238] 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0
 [317] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
 [396] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
 [475] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0
 [554] 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0
 [633] 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [712] 1 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
 [791] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0
 [870] 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
 [949] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0

alumnos.nuevos <- datos.integrados[ind==1,]
alumnos.actuales <- datos.integrados[ind==0,]

alumnos.nuevos

alumnos.actuales

summary(alumnos.nuevos)

 genero admision.letras admision.numeros promedio.preparatoria  edad.ingreso evalucion.socioeconomica nota.conducta   BECA        AM1             AM2       
 1:45   Min.   :44.94   Min.   : 4.878   Min.   : 60.00        17     :28    1: 6                     Min.   : 9.00   0:99   Min.   :1.875   Min.   :1.406  
 2:75   1st Qu.:57.09   1st Qu.:29.176   1st Qu.: 61.26        19     :26    2:15                     1st Qu.:14.00   1:21   1st Qu.:1.875   1st Qu.:1.875  
        Median :59.97   Median :34.947   Median : 69.92        18     :16    3:16                     Median :15.00          Median :1.875   Median :1.875  
        Mean   :60.08   Mean   :35.167   Mean   : 72.31        16     :15    4:83                     Mean   :15.52          Mean   :1.875   Mean   :1.786  
        3rd Qu.:63.28   3rd Qu.:41.556   3rd Qu.: 79.83        20     :13                             3rd Qu.:17.00          3rd Qu.:1.875   3rd Qu.:1.875  
        Max.   :72.00   Max.   :58.992   Max.   :100.00        15     :12                             Max.   :20.00          Max.   :1.875   Max.   :1.875  
                                                               (Other):10                                                                                   
      AM3             AM4             AM5             AM6             AM7             AM8             AM9             AM10            AM11            AM12      
 Min.   :1.031   Min.   :1.031   Min.   :1.375   Min.   :1.125   Min.   :1.406   Min.   :1.125   Min.   :1.562   Min.   :1.219   Min.   :1.562   Min.   :1.438  
 1st Qu.:1.719   1st Qu.:1.812   1st Qu.:1.906   1st Qu.:1.875   1st Qu.:1.688   1st Qu.:1.938   1st Qu.:1.797   1st Qu.:2.000   1st Qu.:1.781   1st Qu.:1.969  
 Median :2.000   Median :1.812   Median :1.906   Median :1.875   Median :1.688   Median :1.938   Median :1.844   Median :2.000   Median :1.969   Median :1.969  
 Mean   :1.847   Mean   :1.670   Mean   :1.808   Mean   :1.739   Mean   :1.641   Mean   :1.825   Mean   :1.778   Mean   :1.885   Mean   :1.892   Mean   :1.897  
 3rd Qu.:2.000   3rd Qu.:1.812   3rd Qu.:1.906   3rd Qu.:1.875   3rd Qu.:1.688   3rd Qu.:1.938   3rd Qu.:1.844   3rd Qu.:2.000   3rd Qu.:1.969   3rd Qu.:1.969  
 Max.   :2.000   Max.   :1.812   Max.   :1.906   Max.   :1.875   Max.   :1.688   Max.   :1.938   Max.   :1.844   Max.   :2.000   Max.   :1.969   Max.   :1.969  
                                                                                                                                                                
      CM1              CM2              CM3              CM4              CM5              CM6             CM7              CM8              CM9        
 Min.   : 7.594   Min.   : 8.218   Min.   : 8.439   Min.   : 7.487   Min.   : 8.214   Min.   : 9.86   Min.   : 9.467   Min.   : 7.685   Min.   : 6.615  
 1st Qu.:11.956   1st Qu.:12.331   1st Qu.:12.463   1st Qu.:11.892   1st Qu.:12.328   1st Qu.:13.32   1st Qu.:13.080   1st Qu.:12.011   1st Qu.:11.369  
 Median :11.956   Median :12.331   Median :12.463   Median :11.892   Median :12.328   Median :15.20   Median :13.080   Median :12.011   Median :11.369  
 Mean   :12.729   Mean   :13.579   Mean   :13.628   Mean   :13.225   Mean   :13.702   Mean   :15.15   Mean   :14.247   Mean   :13.263   Mean   :12.507  
 3rd Qu.:11.956   3rd Qu.:15.775   3rd Qu.:15.951   3rd Qu.:15.189   3rd Qu.:15.771   3rd Qu.:17.09   3rd Qu.:16.773   3rd Qu.:15.348   3rd Qu.:14.492  
 Max.   :18.638   Max.   :18.887   Max.   :18.976   Max.   :18.595   Max.   :18.885   Max.   :19.54   Max.   :19.387   Max.   :18.674   Max.   :18.246  
                                                                                                                                                        
      CM10             CM11             CM12             TM1              TM2              TM3             TM4              TM5              TM6        
 Min.   : 8.036   Min.   : 6.694   Min.   : 8.218   Min.   : 8.648   Min.   : 8.036   Min.   : 8.11   Min.   : 7.457   Min.   : 8.608   Min.   : 8.735  
 1st Qu.:12.221   1st Qu.:11.417   1st Qu.:12.331   1st Qu.:12.589   1st Qu.:12.221   1st Qu.:12.27   1st Qu.:11.874   1st Qu.:12.565   1st Qu.:12.641  
 Median :12.221   Median :11.417   Median :12.331   Median :12.589   Median :12.221   Median :12.27   Median :11.874   Median :12.565   Median :14.415  
 Mean   :13.653   Mean   :12.172   Mean   :13.711   Mean   :13.355   Mean   :13.448   Mean   :13.40   Mean   :13.204   Mean   :13.986   Mean   :14.364  
 3rd Qu.:15.628   3rd Qu.:14.556   3rd Qu.:15.774   3rd Qu.:12.589   3rd Qu.:15.629   3rd Qu.:15.69   3rd Qu.:15.166   3rd Qu.:16.087   3rd Qu.:16.188  
 Max.   :18.814   Max.   :18.278   Max.   :18.887   Max.   :19.059   Max.   :18.814   Max.   :18.84   Max.   :18.583   Max.   :19.043   Max.   :19.094  
                                                                                                                                                        
      TM7              TM8              TM9              TM10             TM11             TM12             VBM1             VBM2            VBM3       
 Min.   : 7.965   Min.   : 8.657   Min.   : 6.836   Min.   : 8.418   Min.   : 7.003   Min.   : 8.624   Min.   : 1.531   Min.   : 1.37   Min.   : 1.336  
 1st Qu.:12.179   1st Qu.:12.594   1st Qu.:11.502   1st Qu.:12.451   1st Qu.:11.602   1st Qu.:12.574   1st Qu.:12.655   1st Qu.:11.85   1st Qu.:11.680  
 Median :12.179   Median :12.594   Median :11.502   Median :12.451   Median :11.602   Median :12.574   Median :12.655   Median :11.85   Median :11.680  
 Mean   :13.151   Mean   :13.943   Mean   :12.666   Mean   :13.918   Mean   :12.397   Mean   :14.001   Mean   :18.942   Mean   :19.67   Mean   :19.901  
 3rd Qu.:15.572   3rd Qu.:16.126   3rd Qu.:14.669   3rd Qu.:15.934   3rd Qu.:14.802   3rd Qu.:16.099   3rd Qu.:12.655   3rd Qu.:27.77   3rd Qu.:27.521  
 Max.   :18.786   Max.   :19.063   Max.   :18.334   Max.   :18.967   Max.   :18.401   Max.   :19.050   Max.   :62.655   Max.   :61.85   Max.   :61.680  
                                                                                                                                                        
      VBM4             VBM5            VBM6             VBM7             VBM8             VBM9            VBM10            VBM11            VBM12       
 Min.   : 2.172   Min.   : 1.40   Min.   : 2.213   Min.   : 2.978   Min.   : 1.933   Min.   : 1.443   Min.   : 2.015   Min.   : 1.554   Min.   : 1.139  
 1st Qu.:15.858   1st Qu.:12.00   1st Qu.:16.063   1st Qu.:19.889   1st Qu.:14.663   1st Qu.:12.216   1st Qu.:15.075   1st Qu.:12.773   1st Qu.:10.694  
 Median :15.858   Median :12.00   Median :25.078   Median :19.889   Median :14.663   Median :12.216   Median :15.075   Median :12.773   Median :10.694  
 Mean   :24.723   Mean   :20.29   Mean   :26.704   Mean   :27.247   Mean   :22.746   Mean   :20.068   Mean   :24.151   Mean   :20.014   Mean   :18.761  
 3rd Qu.:33.787   3rd Qu.:28.00   3rd Qu.:34.094   3rd Qu.:39.834   3rd Qu.:31.994   3rd Qu.:28.324   3rd Qu.:32.612   3rd Qu.:29.159   3rd Qu.:26.040  
 Max.   :65.858   Max.   :62.00   Max.   :66.063   Max.   :69.889   Max.   :64.663   Max.   :62.216   Max.   :65.075   Max.   :62.773   Max.   :60.694  
                                                                                                                                                        
      PDM1              PDM2              PDM3             PDM4              PDM5              PDM6              PDM7              PDM8              PDM9        
 Min.   :  2.328   Min.   :  1.924   Min.   :  1.84   Min.   :  3.929   Min.   :  2.001   Min.   :  4.031   Min.   :  5.945   Min.   :  3.331   Min.   :  2.108  
 1st Qu.: 32.797   1st Qu.: 32.555   1st Qu.: 32.50   1st Qu.: 33.757   1st Qu.: 32.601   1st Qu.: 33.819   1st Qu.: 34.967   1st Qu.: 33.399   1st Qu.: 32.665  
 Median : 32.797   Median : 32.555   Median : 32.50   Median : 33.757   Median : 32.601   Median : 57.066   Median : 34.967   Median : 33.399   Median : 32.665  
 Mean   : 48.853   Mean   : 46.181   Mean   : 48.16   Mean   : 57.651   Mean   : 47.534   Mean   : 63.165   Mean   : 62.721   Mean   : 53.190   Mean   : 47.036  
 3rd Qu.: 32.797   3rd Qu.: 59.244   3rd Qu.: 58.40   3rd Qu.: 79.290   3rd Qu.: 60.011   3rd Qu.: 80.313   3rd Qu.: 99.445   3rd Qu.: 73.315   3rd Qu.: 61.080  
 Max.   :163.275   Max.   :159.244   Max.   :158.40   Max.   :179.290   Max.   :160.011   Max.   :180.313   Max.   :199.445   Max.   :173.315   Max.   :161.080  
                                                                                                                                                                 
     PDM10             PDM11             PDM12              RLM1            RLM2            RLM3            RLM4            RLM5            RLM6            RLM7    
 Min.   :  3.537   Min.   :  2.386   Min.   :  1.347   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.0  
 1st Qu.: 33.522   1st Qu.: 32.832   1st Qu.: 32.208   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.0  
 Median : 33.522   Median : 32.832   Median : 32.208   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :2.000   Median :1.0  
 Mean   : 56.773   Mean   : 47.359   Mean   : 44.220   Mean   :1.367   Mean   :1.442   Mean   :1.458   Mean   :1.958   Mean   :1.475   Mean   :2.092   Mean   :1.8  
 3rd Qu.: 75.374   3rd Qu.: 63.862   3rd Qu.: 53.468   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:3.0  
 Max.   :175.374   Max.   :163.862   Max.   :153.468   Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :5.000   Max.   :4.000   Max.   :5.000   Max.   :5.0  
                                                                                                                                                                    
      RLM8           RLM9          RLM10           RLM11           RLM12           PSEM1          PSEM2      
 Min.   :0.00   Min.   :0.00   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :1.50   Min.   :1.500  
 1st Qu.:1.00   1st Qu.:1.00   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.75   1st Qu.:1.750  
 Median :1.00   Median :1.00   Median :1.000   Median :1.000   Median :1.000   Median :2.00   Median :2.000  
 Mean   :1.45   Mean   :1.45   Mean   :1.958   Mean   :1.383   Mean   :1.475   Mean   :1.89   Mean   :1.923  
 3rd Qu.:2.00   3rd Qu.:2.00   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.00   3rd Qu.:2.000  
 Max.   :4.00   Max.   :4.00   Max.   :5.000   Max.   :4.000   Max.   :4.000   Max.   :2.00   Max.   :2.000  
                                                                                                             

summary(alumnos.actuales)

 genero  admision.letras admision.numeros promedio.preparatoria  edad.ingreso evalucion.socioeconomica nota.conducta   BECA         AM1             AM2       
 1:360   Min.   :44.99   Min.   : 4.986   Min.   : 60.00        17     :172   1: 50                    Min.   : 9.00   0:738   Min.   :1.875   Min.   :1.406  
 2:520   1st Qu.:56.59   1st Qu.:28.187   1st Qu.: 60.00        18     :151   2: 92                    1st Qu.:14.00   1:142   1st Qu.:1.875   1st Qu.:1.875  
         Median :60.04   Median :35.076   Median : 70.11        19     :140   3:136                    Median :15.50           Median :1.875   Median :1.875  
         Mean   :60.05   Mean   :35.107   Mean   : 72.24        16     :125   4:602                    Mean   :15.53           Mean   :1.875   Mean   :1.778  
         3rd Qu.:63.67   3rd Qu.:42.335   3rd Qu.: 81.00        20     :100                            3rd Qu.:17.00           3rd Qu.:1.875   3rd Qu.:1.875  
         Max.   :77.71   Max.   :70.411   Max.   :100.00        15     : 86                            Max.   :20.00           Max.   :1.875   Max.   :1.875  
                                                                (Other):106                                                                                   
      AM3             AM4             AM5             AM6             AM7             AM8             AM9             AM10            AM11            AM12      
 Min.   :1.031   Min.   :1.031   Min.   :1.375   Min.   :1.125   Min.   :1.406   Min.   :1.125   Min.   :1.562   Min.   :1.219   Min.   :1.562   Min.   :1.438  
 1st Qu.:1.719   1st Qu.:1.406   1st Qu.:1.562   1st Qu.:1.438   1st Qu.:1.594   1st Qu.:1.688   1st Qu.:1.797   1st Qu.:2.000   1st Qu.:1.969   1st Qu.:1.969  
 Median :2.000   Median :1.812   Median :1.906   Median :1.875   Median :1.688   Median :1.938   Median :1.844   Median :2.000   Median :1.969   Median :1.969  
 Mean   :1.836   Mean   :1.656   Mean   :1.793   Mean   :1.717   Mean   :1.631   Mean   :1.798   Mean   :1.781   Mean   :1.870   Mean   :1.892   Mean   :1.872  
 3rd Qu.:2.000   3rd Qu.:1.812   3rd Qu.:1.906   3rd Qu.:1.875   3rd Qu.:1.688   3rd Qu.:1.938   3rd Qu.:1.844   3rd Qu.:2.000   3rd Qu.:1.969   3rd Qu.:1.969  
 Max.   :2.000   Max.   :1.812   Max.   :1.906   Max.   :1.875   Max.   :1.688   Max.   :1.938   Max.   :1.844   Max.   :2.000   Max.   :1.969   Max.   :1.969  
                                                                                                                                                                
      CM1              CM2              CM3              CM4              CM5              CM6             CM7              CM8              CM9        
 Min.   : 7.594   Min.   : 8.218   Min.   : 8.439   Min.   : 7.487   Min.   : 8.214   Min.   : 9.86   Min.   : 9.467   Min.   : 7.685   Min.   : 6.615  
 1st Qu.:11.956   1st Qu.:12.331   1st Qu.:12.463   1st Qu.:11.892   1st Qu.:12.328   1st Qu.:13.32   1st Qu.:13.080   1st Qu.:12.011   1st Qu.:11.369  
 Median :11.956   Median :12.331   Median :12.463   Median :11.892   Median :12.328   Median :13.32   Median :13.080   Median :12.011   Median :11.369  
 Mean   :12.754   Mean   :13.566   Mean   :13.711   Mean   :13.042   Mean   :13.566   Mean   :14.67   Mean   :14.648   Mean   :13.229   Mean   :12.491  
 3rd Qu.:11.956   3rd Qu.:15.775   3rd Qu.:15.951   3rd Qu.:15.189   3rd Qu.:15.771   3rd Qu.:17.09   3rd Qu.:16.773   3rd Qu.:15.348   3rd Qu.:14.492  
 Max.   :18.638   Max.   :18.887   Max.   :18.976   Max.   :18.595   Max.   :18.885   Max.   :19.54   Max.   :19.387   Max.   :18.674   Max.   :18.246  
                                                                                                                                                        
      CM10             CM11             CM12             TM1              TM2              TM3             TM4              TM5              TM6        
 Min.   : 8.036   Min.   : 6.694   Min.   : 8.218   Min.   : 8.648   Min.   : 8.036   Min.   : 8.11   Min.   : 7.457   Min.   : 8.608   Min.   : 8.735  
 1st Qu.:12.221   1st Qu.:11.417   1st Qu.:12.331   1st Qu.:12.589   1st Qu.:12.221   1st Qu.:12.27   1st Qu.:11.874   1st Qu.:12.565   1st Qu.:12.641  
 Median :12.221   Median :11.417   Median :12.331   Median :12.589   Median :12.221   Median :12.27   Median :11.874   Median :12.565   Median :12.641  
 Mean   :13.466   Mean   :12.506   Mean   :13.516   Mean   :13.377   Mean   :13.435   Mean   :13.48   Mean   :13.021   Mean   :13.849   Mean   :13.871  
 3rd Qu.:15.628   3rd Qu.:14.556   3rd Qu.:15.774   3rd Qu.:12.589   3rd Qu.:15.629   3rd Qu.:15.69   3rd Qu.:15.166   3rd Qu.:16.087   3rd Qu.:16.188  
 Max.   :18.814   Max.   :18.278   Max.   :18.887   Max.   :19.059   Max.   :18.814   Max.   :18.84   Max.   :18.583   Max.   :19.043   Max.   :19.094  
                                                                                                                                                        
      TM7              TM8              TM9              TM10             TM11             TM12             VBM1             VBM2            VBM3       
 Min.   : 7.965   Min.   : 8.657   Min.   : 6.836   Min.   : 8.418   Min.   : 7.003   Min.   : 8.624   Min.   : 1.531   Min.   : 1.37   Min.   : 1.336  
 1st Qu.:12.179   1st Qu.:12.594   1st Qu.:11.502   1st Qu.:12.451   1st Qu.:11.602   1st Qu.:12.574   1st Qu.:12.655   1st Qu.:11.85   1st Qu.:11.680  
 Median :12.179   Median :12.594   Median :11.502   Median :12.451   Median :11.602   Median :12.574   Median :12.655   Median :11.85   Median :11.680  
 Mean   :13.591   Mean   :13.917   Mean   :12.648   Mean   :13.737   Mean   :12.728   Mean   :13.806   Mean   :19.036   Mean   :19.24   Mean   :19.046  
 3rd Qu.:15.572   3rd Qu.:16.126   3rd Qu.:14.669   3rd Qu.:15.934   3rd Qu.:14.802   3rd Qu.:16.099   3rd Qu.:12.655   3rd Qu.:27.77   3rd Qu.:27.521  
 Max.   :18.786   Max.   :19.063   Max.   :18.334   Max.   :18.967   Max.   :18.401   Max.   :19.050   Max.   :62.655   Max.   :61.85   Max.   :61.680  
                                                                                                                                                        
      VBM4             VBM5            VBM6             VBM7             VBM8             VBM9            VBM10            VBM11            VBM12       
 Min.   : 2.172   Min.   : 1.40   Min.   : 2.213   Min.   : 2.978   Min.   : 1.933   Min.   : 1.443   Min.   : 2.015   Min.   : 1.554   Min.   : 1.139  
 1st Qu.:15.858   1st Qu.:12.00   1st Qu.:16.063   1st Qu.:19.889   1st Qu.:14.663   1st Qu.:12.216   1st Qu.:15.075   1st Qu.:12.773   1st Qu.:10.694  
 Median :15.858   Median :12.00   Median :16.063   Median :19.889   Median :14.663   Median :12.216   Median :15.075   Median :12.773   Median :10.694  
 Mean   :23.866   Mean   :19.57   Mean   :23.758   Mean   :29.663   Mean   :22.626   Mean   :19.946   Mean   :23.188   Mean   :20.502   Mean   :17.805  
 3rd Qu.:33.787   3rd Qu.:28.00   3rd Qu.:34.094   3rd Qu.:39.834   3rd Qu.:31.994   3rd Qu.:28.324   3rd Qu.:32.612   3rd Qu.:29.159   3rd Qu.:26.040  
 Max.   :65.858   Max.   :62.00   Max.   :66.063   Max.   :69.889   Max.   :64.663   Max.   :62.216   Max.   :65.075   Max.   :62.773   Max.   :60.694  
                                                                                                                                                        
      PDM1              PDM2              PDM3             PDM4              PDM5              PDM6              PDM7              PDM8              PDM9        
 Min.   :  2.328   Min.   :  1.924   Min.   :  1.84   Min.   :  3.929   Min.   :  2.001   Min.   :  4.031   Min.   :  5.945   Min.   :  3.331   Min.   :  2.108  
 1st Qu.: 32.797   1st Qu.: 32.555   1st Qu.: 32.50   1st Qu.: 33.757   1st Qu.: 32.601   1st Qu.: 33.819   1st Qu.: 34.967   1st Qu.: 33.399   1st Qu.: 32.665  
 Median : 32.797   Median : 32.555   Median : 32.50   Median : 33.757   Median : 32.601   Median : 33.819   Median : 34.967   Median : 33.399   Median : 32.665  
 Mean   : 49.116   Mean   : 45.275   Mean   : 45.31   Mean   : 55.552   Mean   : 46.111   Mean   : 55.113   Mean   : 69.114   Mean   : 52.821   Mean   : 47.053  
 3rd Qu.: 32.797   3rd Qu.: 59.244   3rd Qu.: 58.40   3rd Qu.: 79.290   3rd Qu.: 60.011   3rd Qu.: 80.313   3rd Qu.: 99.445   3rd Qu.: 73.315   3rd Qu.: 61.080  
 Max.   :163.275   Max.   :159.244   Max.   :158.40   Max.   :179.290   Max.   :160.011   Max.   :180.313   Max.   :199.445   Max.   :173.315   Max.   :161.080  
                                                                                                                                                                 
     PDM10             PDM11             PDM12              RLM1            RLM2            RLM3            RLM4            RLM5            RLM6      
 Min.   :  3.537   Min.   :  2.386   Min.   :  1.347   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
 1st Qu.: 33.522   1st Qu.: 32.832   1st Qu.: 32.208   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
 Median : 33.522   Median : 32.832   Median : 32.208   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :1.000  
 Mean   : 54.183   Mean   : 47.953   Mean   : 42.323   Mean   :1.374   Mean   :1.423   Mean   :1.425   Mean   :1.867   Mean   :1.431   Mean   :1.835  
 3rd Qu.: 75.374   3rd Qu.: 63.862   3rd Qu.: 53.468   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:3.000  
 Max.   :175.374   Max.   :163.862   Max.   :153.468   Max.   :4.000   Max.   :4.000   Max.   :4.000   Max.   :5.000   Max.   :4.000   Max.   :5.000  
                                                                                                                                                      
      RLM7            RLM8           RLM9           RLM10           RLM11           RLM12           PSEM1           PSEM2      
 Min.   :0.000   Min.   :0.00   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :0.000   Min.   :1.500   Min.   :1.500  
 1st Qu.:1.000   1st Qu.:1.00   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.750   1st Qu.:1.750  
 Median :1.000   Median :1.00   Median :1.000   Median :1.000   Median :1.000   Median :1.000   Median :2.000   Median :2.000  
 Mean   :1.976   Mean   :1.44   Mean   :1.444   Mean   :1.886   Mean   :1.435   Mean   :1.411   Mean   :1.893   Mean   :1.897  
 3rd Qu.:3.000   3rd Qu.:2.00   3rd Qu.:2.000   3rd Qu.:3.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000  
 Max.   :5.000   Max.   :4.00   Max.   :4.000   Max.   :5.000   Max.   :4.000   Max.   :4.000   Max.   :2.000   Max.   :2.000  
                                                                                                                               

set.seed(1234)

ind <- sample(x=c(0,1),size=nrow(alumnos.actuales),
              replace=TRUE,prob = c(0.7,0.3))
ind

  [1] 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0
 [81] 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0
[161] 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1
[241] 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1
[321] 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0
[401] 0 0 1 1 0 0 1 0 0 0 1 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0
[481] 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1
[561] 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0
[641] 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0
[721] 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1
[801] 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 1 1 0 1 0 0 0 0 0

alumnos.training <- alumnos.actuales[ind==0,]
alumnos.test <- alumnos.actuales[ind==1,]

str(alumnos.training)

'data.frame':   613 obs. of  82 variables:
 $ genero                  : Factor w/ 2 levels "1","2": 2 2 2 1 2 2 2 1 2 1 ...
 $ admision.letras         : num  60.1 59.1 53.1 57 61.9 ...
 $ admision.numeros        : num  35.2 33.2 21.3 29 38.9 ...
 $ promedio.preparatoria   : num  70.3 67.2 60 61 75.8 ...
 $ edad.ingreso            : Factor w/ 15 levels "11","12","13",..: 8 7 5 6 8 5 7 4 7 10 ...
 $ evalucion.socioeconomica: Factor w/ 4 levels "1","2","3","4": 4 4 4 4 4 4 4 4 4 4 ...
 $ nota.conducta           : num  16 15 13 14 16 13 15 12 15 18 ...
 $ BECA                    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ AM1                     : num  1.88 1.88 1.88 1.88 1.88 ...
 $ AM2                     : num  1.88 1.41 1.41 1.88 1.53 ...
 $ AM3                     : num  2 2 2 2 2 ...
 $ AM4                     : num  1.41 1.81 1.81 1.81 1.81 ...
 $ AM5                     : num  1.91 1.5 1.38 1.56 1.56 ...
 $ AM6                     : num  1.44 1.38 1.12 1.88 1.12 ...
 $ AM7                     : num  1.59 1.69 1.69 1.69 1.59 ...
 $ AM8                     : num  1.12 1.12 1.94 1.69 1.94 ...
 $ AM9                     : num  1.84 1.84 1.84 1.66 1.84 ...
 $ AM10                    : num  2 2 1.22 2 2 ...
 $ AM11                    : num  1.97 1.97 1.97 1.97 1.97 ...
 $ AM12                    : num  1.97 1.97 1.97 1.97 1.78 ...
 $ CM1                     : num  12 12 12 12 12 ...
 $ CM2                     : num  12.33 12.33 15.77 15.77 8.22 ...
 $ CM3                     : num  12.5 12.5 12.5 16 16 ...
 $ CM4                     : num  15.2 11.9 11.9 15.2 15.2 ...
 $ CM5                     : num  12.3 15.8 12.3 12.3 12.3 ...
 $ CM6                     : num  17.1 17.1 13.3 17.1 19.5 ...
 $ CM7                     : num  9.47 13.08 13.08 13.08 19.39 ...
 $ CM8                     : num  12.01 15.35 12.01 7.69 12.01 ...
 $ CM9                     : num  11.4 11.4 11.4 18.2 11.4 ...
 $ CM10                    : num  12.2 12.2 12.2 15.6 15.6 ...
 $ CM11                    : num  11.4 11.4 11.4 11.4 11.4 ...
 $ CM12                    : num  12.33 12.33 12.33 15.77 8.22 ...
 $ TM1                     : num  12.6 12.6 12.6 12.6 12.6 ...
 $ TM2                     : num  12.22 12.22 15.63 15.63 8.04 ...
 $ TM3                     : num  12.3 12.3 12.3 15.7 15.7 ...
 $ TM4                     : num  15.2 11.9 11.9 15.2 15.2 ...
 $ TM5                     : num  12.6 16.1 12.6 12.6 12.6 ...
 $ TM6                     : num  16.2 16.2 12.6 16.2 19.1 ...
 $ TM7                     : num  7.97 12.18 12.18 12.18 18.79 ...
 $ TM8                     : num  12.59 16.13 12.59 8.66 12.59 ...
 $ TM9                     : num  11.5 11.5 11.5 18.3 11.5 ...
 $ TM10                    : num  12.5 12.5 12.5 15.9 15.9 ...
 $ TM11                    : num  11.6 11.6 11.6 11.6 11.6 ...
 $ TM12                    : num  12.57 12.57 12.57 16.1 8.62 ...
 $ VBM1                    : num  12.7 12.7 12.7 12.7 12.7 ...
 $ VBM2                    : num  11.85 11.85 27.77 27.77 1.37 ...
 $ VBM3                    : num  11.7 11.7 11.7 27.5 27.5 ...
 $ VBM4                    : num  33.8 15.9 15.9 33.8 33.8 ...
 $ VBM5                    : num  12 28 12 12 12 ...
 $ VBM6                    : num  34.1 34.1 16.1 34.1 66.1 ...
 $ VBM7                    : num  2.98 19.89 19.89 19.89 69.89 ...
 $ VBM8                    : num  14.66 31.99 14.66 1.93 14.66 ...
 $ VBM9                    : num  12.2 12.2 12.2 62.2 12.2 ...
 $ VBM10                   : num  15.1 15.1 15.1 32.6 32.6 ...
 $ VBM11                   : num  12.8 12.8 12.8 12.8 12.8 ...
 $ VBM12                   : num  10.69 10.69 10.69 26.04 1.14 ...
 $ PDM1                    : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM2                    : num  32.55 32.55 59.24 59.24 1.92 ...
 $ PDM3                    : num  32.5 32.5 32.5 58.4 58.4 ...
 $ PDM4                    : num  79.3 33.8 33.8 79.3 79.3 ...
 $ PDM5                    : num  32.6 60 32.6 32.6 32.6 ...
 $ PDM6                    : num  80.3 80.3 33.8 80.3 180.3 ...
 $ PDM7                    : num  5.94 34.97 34.97 34.97 199.45 ...
 $ PDM8                    : num  33.4 73.31 33.4 3.33 33.4 ...
 $ PDM9                    : num  32.7 32.7 32.7 161.1 32.7 ...
 $ PDM10                   : num  33.5 33.5 33.5 75.4 75.4 ...
 $ PDM11                   : num  32.8 32.8 32.8 32.8 32.8 ...
 $ PDM12                   : num  32.21 32.21 32.21 53.47 1.35 ...
 $ RLM1                    : num  1 1 1 1 1 4 2 1 1 1 ...
 $ RLM2                    : num  1 1 2 2 0 2 1 1 2 1 ...
 $ RLM3                    : num  1 1 1 2 2 2 2 1 2 1 ...
 $ RLM4                    : num  3 1 1 3 3 3 3 1 1 1 ...
 $ RLM5                    : num  1 2 1 1 1 2 1 1 2 1 ...
 $ RLM6                    : num  3 3 1 3 5 0 3 1 3 0 ...
 $ RLM7                    : num  0 1 1 1 5 3 1 1 3 1 ...
 $ RLM8                    : num  1 2 1 0 1 4 0 1 2 1 ...
 $ RLM9                    : num  1 1 1 4 1 2 2 1 1 2 ...
 $ RLM10                   : num  1 1 1 3 3 3 3 5 3 1 ...
 $ RLM11                   : num  1 1 1 1 1 2 1 1 1 1 ...
 $ RLM12                   : num  1 1 1 2 0 2 1 1 1 1 ...
 $ PSEM1                   : num  2 2 2 2 2 1.5 2 2 2 2 ...
 $ PSEM2                   : num  2 1.5 1.5 2 1.5 2 2 2 2 2 ...

set.seed(2020)
wss.alumnos <-vector()
wss.alumnos

logical(0)

centroides.alumnos <- 25
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)

#plot
plot(1:centroides.alumnos  , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")

Analísis de hombro, brazo, codo para seleccionar el centroide

imgPath.codo <- paste(local.path,"/Kmeans-codo-alumnos.png",sep = "")


img.codo.alumnos <- readPNG(imgPath.codo)
plot.new()
rasterImage(img.codo.alumnos,0,0,1,1)

set.seed(2020)
wss.alumnos <-vector()
wss.alumnos

logical(0)

centroides.alumnos <- 10
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)

#plot
plot(1:centroides.alumnos  , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")

imgPath.codo.seleccionado <- paste(local.path,"/Kmeans-codo-alumnos-seleccionado.png",sep = "")


img.codo.sel.alumnos <- readPNG(imgPath.codo.seleccionado)
plot.new()
rasterImage(img.codo.sel.alumnos,0,0,1,1)

clustering.kmeans <- kmeans(x=alumnos.training, centers = 4)
clustering.kmeans$withinss

[1]  867289.6 2325698.5 1619780.3 3420804.7

alumnos.training$genero <- as.numeric(alumnos.training$genero)
alumnos.training$edad.ingreso <- as.numeric(alumnos.training$edad.ingreso)
alumnos.training$evalucion.socioeconomica <- as.numeric(alumnos.training$evalucion.socioeconomica)
alumnos.training$BECA <- as.numeric(alumnos.training$BECA)
sum(is.na(alumnos.training))

[1] 0

sum(is.infinite(alumnos.training))

Error in is.infinite(alumnos.training) : 
  default method not implemented for type 'list'

library(corrplot)
source("http://www.sthda.com/upload/rquery_cormat.r")
rquery.cormat(alumnos.training)

the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroError in hclust(as.dist(1 - corr), method = hclust.method) : 
  NA/NaN/Inf in foreign function call (arg 10)

plot(clustering.kmeans$withinss)